Integrating regulatory motif discovery and genome-wide expression analysis

Abstract

We propose motif regressor for discovering sequence motifs upstream of genes that undergo expression changes in a given condition. The method combines the advantages of matrix-based motif finding and oligomer motif-expression regression analysis, resulting in high sensitivity and specificity. motif regressor is particularly effective in discovering expression-mediating motifs of medium to long width with multiple degenerate positions. When applied to Saccharomyces cerevisiae, motif regressor identified the ROX1 and YAP1 motifs from Rox1p and Yap1p overexpression experiments, respectively; predicted that Gcn4p may have increased activity in YAP1 deletion mutants; reported a group of motifs (including GCN4, PHO4, MET4, STRE, USR1, RAP1, M3A, and M3B) that may mediate the transcriptional response to amino acid starvation; and found all of the known cell-cycle regulation motifs from 18 expression microarrays over two cell cycles.

Figure 1

motif regressor strategy diagram.

Figure 2

Motifs discovered from the amino acid starvation microarray experiment. The motif regressor multiple regression model reported a total 25 significant motifs active in amino acid starvation response. The 25 motifs can be organized into 15 groups, 8 of which represent previously known TF motifs. The identity of the known motifs reflects how the cell responds to amino acid starvation: slowing cell growth (M3A, M3B, and RAP1), responding to general environmental stress (STRE and URS1), and initiating phosphate (PHO4), sulfur (MET4), and amino acid (GCN4) biogenesis and metabolism. Motif matrices are represented as sequence logos (23).

Figure 3

Motif clusters from cell cycle expression time series experiments. The 273 significant motifs reported by motif regressor over two cell cycles are clustered by motif coefficients over the 18 time points. Motif coefficients can be interpreted as the putative influence a particular motif has on the expression of downstream genes. The 20 resulting clusters include the known cell cycle-related TF motifs MCB, SCB, SFF, MCM1, and SWI5. Other motif clusters also have coefficients that fluctuate with the cell cycle, such as STE12, STRE, groups of motifs that resemble MCB and SCB, and some novel G₁ motifs. Five motif clusters have coefficients that do not fluctuate with the cell cycle, including M3B and some motifs of unknown function. The clusters were ordered by first appearance of their cell-cycle influence. This figure was produced using treeview software (30).

Figure 4

Motif effects (coefficients) during the cell cycle. (a) Known cell- cycle-related motifs MCM1, SWI5, MCB, SCB, and SFF have coefficients that fluctuate with the cell cycle. (b) Other cell cycle motifs (STE12, STRE, and motifs in Cluster4 and Cluster6) influence expression through the cell cycle, but to a lesser extent than the known cell cycle regulators. (c) Non-cell-cycle motifs, M3B, and motifs in Cluster18, Cluster19, and Cluster20 showed sharp, low- amplitude fluctuations that correlate to a known experimental artifact that resulted from differential processing of odd- and even-numbered time points (G. Sherlock, personal communication).